code: def test_activation_defaults(quantization_scheme):
      model = nn.Sequential(
          OrderedDict(
              {
                  "conv": nn.Conv2d(1, 20, (3, 3)),
                  "relu": nn.ReLU(),
              }
          )
      )
      config = LinearQuantizerConfig.from_dict(
          {
              "global_config": {
                  "quantization_scheme": quantization_scheme,
                  "milestones": [0, 2, 3, 3],
              }
          }
      )
      quantizer = LinearQuantizer(model, config)
      model = quantizer.prepare(
          example_inputs=torch.randn(1, 1, 28, 28)
      )
  
      assert isinstance(model.conv, torch.nn.intrinsic.qat.ConvReLU2d)
      assert model.activation_post_process_0.dtype == torch.quint8
      if quantization_scheme == "symmetric":
          assert (
              model.activation_post_process_0.qscheme
              == torch.per_tensor_symmetric
          )
      else:
          assert (
              model.activation_post_process_0.qscheme
              == torch.per_tensor_affine
          )
      assert model.activation_post_process_1.dtype == torch.quint8
      assert (
          model.activation_post_process_1.qscheme
          == torch.per_tensor_affine
      )
  
file_path: coremltools/coremltools/test/optimize/torch/quantization/test_quantizer.py
imports: from collections import OrderedDict
  import cattrs
  import pytest
  import torch
  import torch.ao.quantization
  import torch.nn
  import torch.nn.intrinsic
  import torch.nn.intrinsic.qat
  import torch.nn.quantized
  import torch.nn.quantized.modules.utils
  from coremltools.optimize.torch.quantization import LinearQuantizer, LinearQuantizerConfig, ModuleLinearQuantizerConfig
label_desc: Use image node_id 6 for calling a global function with example usage: test_activation_defaults(quantization_scheme) without return types